ToFeT, or "Toxicity-Free Transformers," is a framework designed to identify and mitigate biases, particularly toxicity biases, in transformer-based language models. This approach aims to reduce harmful outputs generated by these models while preserving their performance on legitimate tasks. The concept typically involves implementing various techniques, such as bias detection algorithms, diverse training datasets, and post-processing methods to handle toxic outputs effectively.
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